What is different between base_lr in fine_tune and lr_max in fit_one_cycle.
Hi Hao,
You can see how these two relate in the source code:
def fine_tune(self:Learner, epochs, base_lr=2e-3, freeze_epochs=1, lr_mult=100,
pct_start=0.3, div=5.0, **kwargs):
"Fine tune with `freeze` for `freeze_epochs` then with `unfreeze` from `epochs` using discriminative LR"
self.freeze()
self.fit_one_cycle(freeze_epochs, slice(base_lr), pct_start=0.99, **kwargs)
base_lr /= 2
self.unfreeze()
self.fit_one_cycle(epochs, slice(base_lr/lr_mult, base_lr), pct_start=pct_start, div=div, **kwargs)
fine_tune
calls fit_one_cycle
twice, using a slice object the second time so the lr_max
is smaller (base_lr/lr_mult
) for the first layer, and gradually increases to base_lr
for the last layer.
lr_max
is the maximum learning rate of the curve drawn by fit_one_cycle
.
Hope this is clear, fastai’s source code is very readable, if not, let me know and we can explore a bit further.
K.
1 Like
Thanks a lot.
1 Like